import numpy as np
import pandas as pd
import plotly.offline as pyo
import plotly.graph_objects as go
pyo.init_notebook_mode()
dfEconomy = pd.read_csv('Modified Data/EconomicIndicators.csv')
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
display(dfEconomy)
| Date | GDP (current US$) | GDP (constant 2015 US$) | GDP growth (annual %) | GDP per capita (current US$) | GDP per capita (constant 2015 US$) | GDP per capita growth (annual %) | GDP, PPP (current international $) | GDP, PPP (constant 2017 international $) | GNI (current US$) | GNI (constant 2015 US$) | GNI growth (annual %) | Exports of goods and services (BoP, current US$) | Exports of goods and services (constant 2015 US$) | Exports of goods and services (annual % growth) | Imports of goods and services (BoP, current US$) | Imports of goods and services (constant 2015 US$) | Imports of goods and services (annual % growth) | Inflation, consumer prices (annual %) | External debt stocks, total (DOD, current US$) | External debt (% of GDP) | GDP, PPP Growth (annual %) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1960 | 3.749265e+09 | 1.705191e+10 | NaN | 81.586947 | 371.062860 | NaN | NaN | NaN | 3.743806e+09 | 1.702708e+10 | NaN | 3.711538e+08 | 1.688032e+09 | NaN | 2.485962e+09 | 1.130632e+10 | NaN | 6.947368 | NaN | NaN | NaN |
| 1 | 1961 | 4.118648e+09 | 1.807286e+10 | 5.987346 | 87.517372 | 384.031276 | 3.494938 | NaN | NaN | 4.113188e+09 | 1.804891e+10 | 6.001208 | 3.962018e+08 | 1.738556e+09 | 2.993079 | 2.703655e+09 | 1.186379e+10 | 4.930638 | 1.640420 | NaN | NaN | NaN |
| 2 | 1962 | 4.310164e+09 | 1.888304e+10 | 4.482859 | 89.493336 | 392.074799 | 2.094497 | NaN | NaN | 4.304914e+09 | 1.886004e+10 | 4.494103 | 4.090443e+08 | 1.792044e+09 | 3.076539 | 2.729544e+09 | 1.195827e+10 | 0.796334 | -0.516462 | NaN | NaN | NaN |
| 3 | 1963 | 4.630827e+09 | 2.052376e+10 | 8.688832 | 93.883886 | 416.092027 | 6.125675 | NaN | NaN | 4.618647e+09 | 2.046978e+10 | 8.535162 | 5.619018e+08 | 2.490341e+09 | 38.966518 | 3.176099e+09 | 1.407642e+10 | 17.712905 | 1.456488 | NaN | NaN | NaN |
| 4 | 1964 | 5.204956e+09 | 2.207736e+10 | 7.569757 | 102.961207 | 436.720611 | 4.957698 | NaN | NaN | 5.191096e+09 | 2.201857e+10 | 7.566232 | 5.723116e+08 | 2.427519e+09 | -2.522625 | 3.537682e+09 | 1.500545e+10 | 6.599837 | 4.179587 | NaN | NaN | NaN |
| 5 | 1965 | 5.929231e+09 | 2.437768e+10 | 10.419366 | 114.372019 | 470.233701 | 7.673805 | NaN | NaN | 5.904242e+09 | 2.427494e+10 | 10.247571 | 5.937100e+08 | 2.441003e+09 | 0.555479 | 4.105651e+09 | 1.688014e+10 | 12.493420 | 5.568635 | NaN | NaN | NaN |
| 6 | 1966 | 6.561109e+09 | 2.578914e+10 | 5.789952 | 123.330471 | 484.763527 | 3.089916 | NaN | NaN | 6.530659e+09 | 2.566945e+10 | 5.744658 | 6.875562e+08 | 2.702513e+09 | 10.713203 | 3.989464e+09 | 1.568101e+10 | -7.103777 | 7.227622 | NaN | NaN | NaN |
| 7 | 1967 | 7.464511e+09 | 2.718191e+10 | 5.400613 | 136.638093 | 497.565621 | 2.640895 | NaN | NaN | 7.414512e+09 | 2.699984e+10 | 5.182761 | 7.780730e+08 | 2.833341e+09 | 4.841005 | 4.900859e+09 | 1.784641e+10 | 13.809021 | 6.811400 | NaN | NaN | NaN |
| 8 | 1968 | 8.041999e+09 | 2.914803e+10 | 7.233221 | 143.287946 | 519.343746 | 4.376935 | NaN | NaN | 7.976001e+09 | 2.890882e+10 | 7.070367 | 8.342945e+08 | 3.023881e+09 | 6.724890 | 5.249102e+09 | 1.902524e+10 | 6.605467 | 0.170627 | NaN | NaN | NaN |
| 9 | 1969 | 8.683116e+09 | 3.075348e+10 | 5.507900 | 150.547804 | 533.203577 | 2.668720 | NaN | NaN | 8.608118e+09 | 3.048785e+10 | 5.462100 | 9.311302e+08 | 3.297836e+09 | 9.059728 | 4.491420e+09 | 1.590751e+10 | -16.387338 | 3.186987 | NaN | NaN | NaN |
| 10 | 1970 | 1.002751e+10 | 3.424506e+10 | 11.353462 | 169.124000 | 577.577323 | 8.322102 | NaN | NaN | 9.933511e+09 | 3.392405e+10 | 11.270708 | 1.118591e+09 | 3.820114e+09 | 15.836999 | 6.893466e+09 | 2.354196e+10 | 47.992689 | 5.349841 | 3.406743e+09 | 33.973965 | NaN |
| 11 | 1971 | 1.066590e+10 | 3.440546e+10 | 0.468373 | 175.198920 | 565.146954 | -2.152157 | NaN | NaN | 1.060271e+10 | 3.420163e+10 | 0.818254 | 1.194777e+09 | 3.854044e+09 | 0.888187 | 6.567159e+09 | 2.118398e+10 | -10.016073 | 4.730691 | 3.795845e+09 | 35.588613 | NaN |
| 12 | 1972 | 9.415016e+09 | 3.468531e+10 | 0.813406 | 150.617211 | 554.880102 | -1.816669 | NaN | NaN | 9.280297e+09 | 3.418900e+10 | -0.036931 | 8.663720e+08 | 3.191751e+09 | -17.184375 | 3.920867e+09 | 1.444464e+10 | -31.813375 | 5.183238 | 4.072099e+09 | 43.251106 | NaN |
| 13 | 1973 | 6.383429e+09 | 3.713558e+10 | 7.064264 | 99.297932 | 577.665326 | 4.106333 | NaN | NaN | 6.304784e+09 | 3.667805e+10 | 7.280269 | 5.801770e+08 | 3.375178e+09 | 5.746911 | 2.204229e+09 | 1.282309e+10 | -11.225944 | 23.070084 | 4.566896e+09 | 71.542984 | NaN |
| 14 | 1974 | 8.899192e+09 | 3.845025e+10 | 3.540192 | 134.532180 | 581.265753 | 0.623272 | NaN | NaN | 8.825592e+09 | 3.813225e+10 | 3.964748 | 6.462350e+08 | 2.792152e+09 | -17.273927 | 3.431544e+09 | 1.482648e+10 | 15.623299 | 26.663035 | 5.121326e+09 | 57.548218 | NaN |
| 15 | 1975 | 1.123061e+10 | 4.006955e+10 | 4.211416 | 164.848096 | 588.159566 | 1.186000 | NaN | NaN | 1.113531e+10 | 3.972953e+10 | 4.188789 | 8.729413e+08 | 3.114557e+09 | 11.546830 | 3.891331e+09 | 1.388383e+10 | -6.357856 | 20.904509 | 5.752877e+09 | 51.224991 | NaN |
| 16 | 1976 | 1.316808e+10 | 4.213561e+10 | 5.156190 | 187.496907 | 599.958055 | 2.006001 | NaN | NaN | 1.302388e+10 | 4.167419e+10 | 4.894763 | 1.449756e+09 | 3.555345e+09 | 14.152517 | 2.643865e+09 | 1.485010e+10 | 6.959644 | 7.158324 | 6.802352e+09 | 51.657882 | NaN |
| 17 | 1977 | 1.512606e+10 | 4.379899e+10 | 3.947698 | 208.776120 | 604.531768 | 0.762339 | NaN | NaN | 1.494521e+10 | 4.327532e+10 | 3.842014 | 1.438288e+09 | 2.930241e+09 | -17.582100 | 2.993491e+09 | 1.643440e+10 | 10.668627 | 10.132968 | 7.564124e+09 | 50.007233 | NaN |
| 18 | 1978 | 1.781152e+10 | 4.732417e+10 | 8.048534 | 238.155833 | 632.766351 | 4.670488 | NaN | NaN | 1.763026e+10 | 4.684258e+10 | 8.243173 | 1.805675e+09 | 3.304059e+09 | 12.757248 | 3.855733e+09 | 1.755601e+10 | 6.824757 | 6.138693 | 8.329289e+09 | 46.763506 | NaN |
| 19 | 1979 | 1.968838e+10 | 4.910282e+10 | 3.758436 | 254.347761 | 634.343194 | 0.249198 | NaN | NaN | 1.945476e+10 | 4.852016e+10 | 3.581310 | 2.503913e+09 | 3.743810e+09 | 13.309419 | 5.100890e+09 | 2.286609e+10 | 30.246484 | 8.267047 | 8.918891e+09 | 45.300268 | NaN |
| 20 | 1980 | 2.365444e+10 | 5.411902e+10 | 10.215704 | 293.391890 | 671.251494 | 5.818349 | NaN | NaN | 2.337231e+10 | 5.347352e+10 | 10.208870 | 3.232901e+09 | 4.474111e+09 | 19.506883 | 6.347873e+09 | 2.378178e+10 | 4.004601 | 11.938231 | 9.931199e+09 | 41.984493 | NaN |
| 21 | 1981 | 2.810061e+10 | 5.840566e+10 | 7.920764 | 333.458392 | 693.076044 | 3.251322 | NaN | NaN | 2.783846e+10 | 5.786081e+10 | 8.204600 | 3.389497e+09 | 5.290494e+09 | 18.246841 | 6.601898e+09 | 1.881328e+10 | -20.892062 | 11.879914 | 1.058077e+10 | 37.653165 | NaN |
| 22 | 1982 | 3.072597e+10 | 6.222392e+10 | 6.537487 | 349.841762 | 708.473153 | 2.221561 | NaN | NaN | 3.040362e+10 | 6.157111e+10 | 6.412473 | 3.154730e+09 | 4.973868e+09 | -5.984815 | 6.721840e+09 | 1.872425e+10 | -0.473207 | 5.903529 | 1.170399e+10 | 38.091535 | NaN |
| 23 | 1983 | 2.869189e+10 | 6.644169e+10 | 6.778378 | 315.017266 | 729.484209 | 2.965681 | NaN | NaN | 2.826895e+10 | 6.546229e+10 | 6.319800 | 3.662621e+09 | 6.196074e+09 | 24.572534 | 6.607695e+09 | 2.080395e+10 | 11.106973 | 6.362033 | 1.202622e+10 | 41.915062 | NaN |
| 24 | 1984 | 3.115183e+10 | 6.980710e+10 | 5.065206 | 331.388766 | 742.598190 | 1.797706 | NaN | NaN | 3.070697e+10 | 6.881024e+10 | 5.114330 | 3.286531e+09 | 5.967308e+09 | -3.692100 | 7.355744e+09 | 2.230861e+10 | 7.232582 | 6.087167 | 1.222789e+10 | 39.252547 | NaN |
| 25 | 1985 | 3.114492e+10 | 7.510694e+10 | 7.592115 | 320.679810 | 773.329248 | 4.138316 | NaN | NaN | 3.063643e+10 | 7.388069e+10 | 7.368739 | 3.509102e+09 | 5.945824e+09 | -0.360031 | 7.090127e+09 | 2.429777e+10 | 8.916550 | 5.614839 | 1.346488e+10 | 43.232996 | NaN |
| 26 | 1986 | 3.189907e+10 | 7.923906e+10 | 5.501654 | 317.029798 | 787.519613 | 1.834971 | NaN | NaN | 3.125540e+10 | 7.749968e+10 | 4.898425 | 4.035983e+09 | 7.896553e+09 | 32.808379 | 7.200329e+09 | 2.369972e+10 | -2.461337 | 3.506414 | 1.495441e+10 | 46.880387 | NaN |
| 27 | 1987 | 3.335153e+10 | 8.435184e+10 | 6.452343 | 319.915392 | 809.121840 | 2.743072 | NaN | NaN | 3.265316e+10 | 8.241636e+10 | 6.344130 | 4.928011e+09 | 8.843321e+09 | 11.989638 | 7.570955e+09 | 2.417180e+10 | 1.991929 | 4.681219 | 1.679767e+10 | 50.365517 | NaN |
| 28 | 1988 | 3.847274e+10 | 9.078390e+10 | 7.625279 | 356.335215 | 840.842056 | 3.920326 | NaN | NaN | 3.763885e+10 | 8.864327e+10 | 7.555432 | 5.282184e+09 | 8.461946e+09 | -4.312569 | 8.623660e+09 | 2.334876e+10 | -3.404947 | 8.837937 | 1.706517e+10 | 44.356508 | NaN |
| 29 | 1989 | 4.017102e+10 | 9.528657e+10 | 4.959769 | 359.728480 | 853.284151 | 1.479718 | NaN | NaN | 3.928197e+10 | 9.301877e+10 | 4.936072 | 6.005657e+09 | 9.627554e+09 | 13.774694 | 9.112948e+09 | 2.529190e+10 | 8.322199 | 7.844265 | 1.834819e+10 | 45.675190 | NaN |
| 30 | 1990 | 4.001042e+10 | 9.953500e+10 | 4.458587 | 346.668516 | 862.416565 | 1.070266 | 2.327968e+11 | 3.533440e+11 | 3.903798e+10 | 9.693854e+10 | 4.213955 | 6.834726e+09 | 9.735861e+09 | 1.124974 | 1.020537e+10 | 2.440678e+10 | -3.499598 | 9.052132 | 2.066338e+10 | 51.644981 | NaN |
| 31 | 1991 | 4.562523e+10 | 1.045730e+11 | 5.061568 | 382.750576 | 877.264310 | 1.721644 | 2.528511e+11 | 3.712288e+11 | 4.445642e+10 | 1.017412e+11 | 4.954291 | 7.941736e+09 | 1.299399e+10 | 33.465230 | 1.099746e+10 | 2.260143e+10 | -7.396928 | 11.791270 | 2.336332e+10 | 51.207007 | 5.061568 |
| 32 | 1992 | 4.888461e+10 | 1.126313e+11 | 7.705898 | 399.465049 | 920.377228 | 4.914473 | 2.785418e+11 | 3.998353e+11 | 4.760841e+10 | 1.095159e+11 | 7.641726 | 8.472574e+09 | 1.478989e+10 | 13.820981 | 1.239996e+10 | 2.957242e+10 | 30.843125 | 9.509041 | 2.491962e+10 | 50.976410 | 7.705898 |
| 33 | 1993 | 5.180995e+10 | 1.146111e+11 | 1.757748 | 412.675000 | 912.896796 | -0.812757 | 2.901563e+11 | 4.068634e+11 | 5.029901e+10 | 1.110863e+11 | 1.433939 | 8.366369e+09 | 1.498472e+10 | 1.317358 | 1.201866e+10 | 3.396225e+10 | 14.844358 | 9.973665 | 2.455156e+10 | 47.387735 | 1.757748 |
| 34 | 1994 | 5.229346e+10 | 1.188946e+11 | 3.737416 | 404.606760 | 919.915416 | 0.768829 | 3.074283e+11 | 4.220696e+11 | 5.068341e+10 | 1.150187e+11 | 3.539918 | 8.869456e+09 | 1.545085e+10 | 3.110663 | 1.188476e+10 | 3.029795e+10 | -10.789340 | 12.368194 | 2.739083e+10 | 52.379071 | 3.737416 |
| 35 | 1995 | 6.063602e+10 | 1.247949e+11 | 4.962609 | 455.507603 | 937.479237 | 1.909287 | 3.294512e+11 | 4.430152e+11 | 5.885066e+10 | 1.208556e+11 | 5.074791 | 1.021360e+10 | 1.497568e+10 | -3.075315 | 1.418530e+10 | 3.150071e+10 | 3.969777 | 12.343579 | 3.024109e+10 | 49.873137 | 4.962609 |
| 36 | 1996 | 6.332012e+10 | 1.308432e+11 | 4.846581 | 461.399865 | 953.425408 | 1.700963 | 3.517428e+11 | 4.644863e+11 | 6.135301e+10 | 1.264826e+11 | 4.655964 | 1.052348e+10 | 1.527438e+10 | 1.994558 | 1.562253e+10 | 3.578274e+10 | 13.593456 | 10.373809 | 2.985095e+10 | 47.142919 | 4.846581 |
| 37 | 1997 | 6.243330e+10 | 1.321704e+11 | 1.014396 | 441.754634 | 935.188373 | -1.912791 | 3.614380e+11 | 4.691980e+11 | 6.024700e+10 | 1.272020e+11 | 0.568747 | 9.975895e+09 | 1.427605e+10 | -6.535973 | 1.340845e+10 | 3.442828e+10 | -3.785257 | 11.375493 | 3.009512e+10 | 48.203642 | 1.014396 |
| 38 | 1998 | 6.219196e+10 | 1.355411e+11 | 2.550234 | 427.506327 | 931.706805 | -0.372285 | 3.748273e+11 | 4.811637e+11 | 5.985318e+10 | 1.298858e+11 | 2.109860 | 9.155000e+09 | 1.345804e+10 | -5.729972 | 1.199600e+10 | 3.249302e+10 | -5.621130 | 6.228004 | 3.231019e+10 | 51.952370 | 2.550234 |
| 39 | 1999 | 6.297386e+10 | 1.405021e+11 | 3.660133 | 420.682602 | 938.592245 | 0.739014 | 3.940220e+11 | 4.987749e+11 | 6.116586e+10 | 1.360770e+11 | 4.766671 | 8.945000e+09 | 1.307444e+10 | -2.850317 | 1.156500e+10 | 3.073793e+10 | -5.401426 | 4.142637 | 3.419227e+10 | 54.295981 | 3.660133 |
| 40 | 2000 | 8.201774e+10 | 1.464876e+11 | 4.260088 | 531.306496 | 948.938556 | 1.102322 | 4.201147e+11 | 5.200232e+11 | 7.999774e+10 | 1.425981e+11 | 4.792214 | 9.997000e+09 | 1.516845e+10 | 16.016001 | 1.202600e+10 | 3.004631e+10 | -2.250047 | 4.366665 | 3.311199e+10 | 40.371739 | 4.260088 |
| 41 | 2001 | 7.948440e+10 | 1.516944e+11 | 3.554418 | 499.218306 | 952.747891 | 0.401431 | 4.448488e+11 | 5.385070e+11 | 7.732440e+10 | 1.472903e+11 | 3.290492 | 1.047100e+10 | 1.701566e+10 | 12.177984 | 1.195200e+10 | 3.069417e+10 | 2.156206 | 3.148261 | 3.204618e+10 | 40.317568 | 3.554418 |
| 42 | 2002 | 7.990499e+10 | 1.554994e+11 | 2.508338 | 489.425527 | 952.448161 | -0.031460 | 4.631141e+11 | 5.520145e+11 | 7.758599e+10 | 1.506694e+11 | 2.294197 | 1.215800e+10 | 1.870942e+10 | 9.954177 | 1.256600e+10 | 3.162925e+10 | 3.046429 | 3.290345 | 3.396703e+10 | 42.509281 | 2.508338 |
| 43 | 2003 | 9.176054e+10 | 1.644826e+11 | 5.777034 | 549.870377 | 985.653665 | 3.486332 | 4.995364e+11 | 5.839046e+11 | 8.954954e+10 | 1.602760e+11 | 6.375903 | 1.477500e+10 | 2.401902e+10 | 28.379245 | 1.521000e+10 | 3.517791e+10 | 11.219542 | 2.914135 | 3.666857e+10 | 39.961154 | 5.777034 |
| 44 | 2004 | 1.077597e+11 | 1.768959e+11 | 7.546860 | 631.471171 | 1036.608932 | 5.169693 | 5.516573e+11 | 6.279711e+11 | 1.055527e+11 | 1.730812e+11 | 7.989505 | 1.602701e+10 | 2.365229e+10 | -1.526817 | 2.200700e+10 | 3.216049e+10 | -8.577605 | 7.444625 | 3.656194e+10 | 33.929147 | 7.546860 |
| 45 | 2005 | 1.200553e+11 | 1.884273e+11 | 6.518778 | 688.500588 | 1080.604844 | 4.244215 | 6.060453e+11 | 6.689071e+11 | 1.176693e+11 | 1.845085e+11 | 6.602257 | 1.910500e+10 | 2.642603e+10 | 11.727160 | 2.927520e+10 | 4.487250e+10 | 39.526807 | 9.063327 | 3.426005e+10 | 28.536889 | 6.518778 |
| 46 | 2006 | 1.372641e+11 | 1.995426e+11 | 5.898984 | 770.843339 | 1120.585448 | 3.699836 | 6.615990e+11 | 7.083658e+11 | 1.345971e+11 | 1.955644e+11 | 5.992102 | 2.054000e+10 | 2.903976e+10 | 9.890712 | 3.509800e+10 | 5.325585e+10 | 18.682598 | 7.921084 | 3.743078e+10 | 27.269174 | 5.898984 |
| 47 | 2007 | 1.523857e+11 | 2.091862e+11 | 4.832817 | 837.631538 | 1149.851392 | 2.611666 | 7.123169e+11 | 7.425999e+11 | 1.488037e+11 | 2.041747e+11 | 4.402776 | 2.194600e+10 | 2.947818e+10 | 1.509728 | 3.758600e+10 | 5.108819e+10 | -4.070269 | 7.598684 | 4.230609e+10 | 27.762503 | 4.832817 |
| 48 | 2008 | 1.700778e+11 | 2.127453e+11 | 1.701405 | 914.731489 | 1144.210357 | -0.490588 | 7.383305e+11 | 7.552345e+11 | 1.661548e+11 | 2.078602e+11 | 1.805072 | 2.547250e+10 | 2.813631e+10 | -4.552083 | 4.792900e+10 | 5.408293e+10 | 5.861904 | 20.286121 | 4.982630e+10 | 29.296179 | 1.701405 |
| 49 | 2009 | 1.681528e+11 | 2.187695e+11 | 2.831659 | 884.441014 | 1150.672105 | 0.564734 | 7.641038e+11 | 7.766202e+11 | 1.637458e+11 | 2.130407e+11 | 2.492313 | 2.231300e+10 | 2.719060e+10 | -3.361179 | 3.515143e+10 | 4.548065e+10 | -15.905721 | 13.647765 | 5.666292e+10 | 33.697286 | 2.831659 |
| 50 | 2010 | 1.771656e+11 | 2.222844e+11 | 1.606689 | 911.090445 | 1143.117978 | -0.656497 | 7.857111e+11 | 7.890980e+11 | 1.738836e+11 | 2.181474e+11 | 2.397036 | 2.805600e+10 | 3.146160e+10 | 15.707660 | 4.001600e+10 | 4.745768e+10 | 4.346972 | 12.938871 | 6.312425e+10 | 35.630074 | 1.606689 |
| 51 | 2011 | 2.135874e+11 | 2.283937e+11 | 2.748406 | 1075.450496 | 1150.002831 | 0.602287 | 8.240791e+11 | 8.107856e+11 | 2.105704e+11 | 2.251278e+11 | 3.199846 | 3.143300e+10 | 3.220793e+10 | 2.372198 | 4.715100e+10 | 4.740107e+10 | -0.119291 | 11.916093 | 6.474256e+10 | 30.311974 | 2.748406 |
| 52 | 2012 | 2.243836e+11 | 2.364036e+11 | 3.507033 | 1109.679115 | 1169.123158 | 1.662633 | 8.470947e+11 | 8.392202e+11 | 2.211386e+11 | 2.329939e+11 | 3.494090 | 3.137400e+10 | 2.737647e+10 | -15.000842 | 4.890200e+10 | 4.593374e+10 | -3.095572 | 9.682352 | 6.366952e+10 | 28.375298 | 3.507033 |
| 53 | 2013 | 2.312186e+11 | 2.467969e+11 | 4.396457 | 1126.041261 | 1201.908371 | 2.804257 | 8.833753e+11 | 8.761161e+11 | 2.275496e+11 | 2.429033e+11 | 4.253064 | 3.004300e+10 | 3.109442e+10 | 13.580834 | 4.916700e+10 | 4.676518e+10 | 1.810099 | 7.692156 | 6.008782e+10 | 25.987455 | 4.396457 |
| 54 | 2014 | 2.443609e+11 | 2.583340e+11 | 4.674708 | 1173.392454 | 1240.489561 | 3.209994 | 9.317307e+11 | 9.170720e+11 | 2.404059e+11 | 2.541608e+11 | 4.634549 | 3.059400e+10 | 3.063417e+10 | -1.480176 | 5.114100e+10 | 4.688412e+10 | 0.254337 | 7.189384 | 6.420272e+10 | 26.273730 | 4.674708 |
| 55 | 2015 | 2.705561e+11 | 2.705561e+11 | 4.731147 | 1282.443153 | 1282.443153 | 3.382019 | 9.815578e+11 | 9.604600e+11 | 2.659571e+11 | 2.659571e+11 | 4.641291 | 2.860400e+10 | 2.869089e+10 | -6.343528 | 4.862200e+10 | 4.613053e+10 | -1.607358 | 2.529328 | 6.861419e+10 | 25.360426 | 4.731147 |
| 56 | 2016 | 3.136299e+11 | 2.855091e+11 | 5.526736 | 1468.821421 | 1337.123374 | 4.263754 | 1.010730e+12 | 1.013542e+12 | 3.082849e+11 | 2.805833e+11 | 5.499429 | 2.686921e+10 | 2.823088e+10 | -1.603304 | 5.190611e+10 | 5.352149e+10 | 16.021858 | 3.765119 | 7.505215e+10 | 23.930166 | 5.526736 |
| 57 | 2017 | 3.392056e+11 | 2.981646e+11 | 4.432626 | 1567.640986 | 1377.969674 | 3.054789 | 1.058469e+12 | 1.058469e+12 | 3.341916e+11 | 2.936706e+11 | 4.664352 | 2.949144e+10 | 2.892856e+10 | 2.471341 | 6.449806e+10 | 6.368181e+10 | 18.983621 | 4.085374 | 9.166207e+10 | 27.022568 | 4.432626 |
| 58 | 2018 | 3.561282e+11 | 3.165068e+11 | 6.151703 | 1620.742857 | 1440.425395 | 4.532445 | 1.150425e+12 | 1.123582e+12 | 3.506912e+11 | 3.115869e+11 | 6.100812 | 3.077489e+10 | 3.183317e+10 | 10.040608 | 6.842288e+10 | 7.370106e+10 | 15.733289 | 5.078057 | 9.922396e+10 | 27.861863 | 6.151703 |
| 59 | 2019 | 3.209095e+11 | 3.244120e+11 | 2.497637 | 1437.165907 | 1452.851573 | 0.862674 | 1.200250e+12 | 1.151645e+12 | 3.152995e+11 | 3.186270e+11 | 2.259429 | 3.067015e+10 | 3.602634e+10 | 13.172326 | 5.797608e+10 | 7.931113e+10 | 7.611928 | 10.578362 | 1.078829e+11 | 33.617870 | 2.497637 |
| 60 | 2020 | 3.004257e+11 | 3.202787e+11 | -1.274087 | 1322.315036 | 1409.697601 | -2.970295 | 1.199240e+12 | 1.136973e+12 | 2.949667e+11 | 3.142941e+11 | -1.359869 | 2.733307e+10 | 3.657228e+10 | 1.515410 | 5.209794e+10 | 7.529519e+10 | -5.063519 | 9.739993 | 1.156953e+11 | 38.510473 | -1.274087 |
| 61 | 2021 | 3.482625e+11 | 3.410555e+11 | 6.487087 | 1505.010193 | 1473.864900 | 4.551848 | 1.330101e+12 | 1.210729e+12 | 3.438625e+11 | 3.365756e+11 | 7.089386 | 3.556588e+10 | 3.895724e+10 | 6.521199 | 7.639213e+10 | 8.620243e+10 | 14.485972 | 9.496211 | 1.304331e+11 | 37.452508 | 6.487087 |
| 62 | 2022 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.856314e+10 | NaN | NaN | 7.589625e+10 | NaN | NaN | 19.873860 | NaN | NaN | NaN |
fig = go.Figure()
fig.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP (current US$)'], name= 'GDP', line={'color': 'blue', 'width': 2}))
fig.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GNI (current US$)'], name= 'GNI', line={'color': '#064d02', 'width': 2}))
fig.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP (constant 2015 US$)'], name= 'GDP (Constant 2015)', line={'color': 'cyan', 'width': 2}))
fig.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GNI (constant 2015 US$)'], name= 'GNI (Constant 2015)', line={'color': '#90ee90', 'width': 2}))
fig.update_layout(
title="Nominal and Constant GDP and GNI",
xaxis_title="Year",
yaxis_title="Value in USD $")
fig.show()
fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP per capita (current US$)'], name='GDP per capita', line={'color': 'blue', 'width': 2}))
fig2.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP per capita (constant 2015 US$)'], name='GDP per capita (Constant 2015)', line={'color': 'cyan', 'width': 2}))
fig2.update_layout(
title="Nominal and Constant GDP per capita",
xaxis_title="Year",
yaxis_title="Value in USD $"
)
fig2.show()
fig3 = go.Figure()
fig3.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP growth (annual %)'], name='GDP', mode='lines+markers', line={'color': 'blue', 'width': 2}))
fig3.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP per capita growth (annual %)'], name='GDP per capita', mode='lines+markers', line={'color': 'cyan', 'width': 2}))
fig3.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GNI growth (annual %)'], name='GNI', mode='lines+markers', line={'color': 'green', 'width': 2}))
fig3.update_layout(
title="Nominal and Constant GDP and GNI Growth Rates",
xaxis_title="Year",
yaxis_title="Percentage %"
)
fig3.show()
fig4 = go.Figure()
fig4.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP, PPP (current international $)'], name='GDP PPP', line={'color': '#54518a', 'width': 2}))
fig4.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP, PPP (constant 2017 international $)'], name='GDP PPP (Constant 2017)', line={'color': '#050157', 'width': 2}))
fig4.update_layout(
xaxis = dict(
tickmode = 'linear',
range = [1990, 2022],
dtick = 5
),
yaxis = dict(
tickmode = 'linear',
range = [0, 1400000000000],
dtick = 100000000000,
separatethousands= True
),
title="Nominal and Constant GDP based on Purchasing Power Parity",
xaxis_title="Year",
yaxis_title="Value in USD $"
)
fig4.show()
fig5 = go.Figure()
fig5.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['GDP, PPP Growth (annual %)'], name='GDP', mode='lines+markers', line={'color': '#54518a', 'width': 2}))
fig5.update_layout(
xaxis = dict(
tickmode = 'linear',
range = [1990, 2022],
dtick = 5
),
title="GDP PPP Growth Rate",
xaxis_title="Year",
yaxis_title="Percentage %"
)
fig5.show()
fig6 = go.Figure()
fig6.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['External debt stocks, total (DOD, current US$)'], name='External Debt', line={'color': 'red', 'width': 2}))
fig6.update_layout(
xaxis = dict(
tickmode = 'linear',
range = [1970, 2022],
dtick = 10
),
title="External Debt (Nominal)",
xaxis_title="Year",
yaxis_title="Value in USD $"
)
fig6.show()
fig7 = go.Figure()
fig7.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['External debt (% of GDP)'], name='External Debt', line={'color': 'red', 'width': 2}))
fig7.update_layout(
xaxis = dict(
tickmode = 'linear',
range = [1970, 2022],
dtick = 10
),
title="External Debt % of GDP",
xaxis_title="Year",
yaxis_title="Percentage %"
)
fig7.show()
fig8 = go.Figure()
fig8.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Inflation, consumer prices (annual %)'], name= 'Inflation', line={'color': 'firebrick', 'width': 2}))
fig8.update_layout(
title="Inflation of PKR",
xaxis_title="Year",
yaxis_title="Percentage %")
fig8.show()
fig9 = go.Figure()
fig9.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Exports of goods and services (BoP, current US$)'], name= 'Exports', line={'color': '#067d00', 'width': 2}))
fig9.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Exports of goods and services (constant 2015 US$)'], name= 'Exports (Constant 2015)', line={'color': '#1c421b', 'width': 2}))
fig9.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Imports of goods and services (BoP, current US$)'], name= 'Imports', line={'color': '#d4b361', 'width': 2}))
fig9.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Imports of goods and services (constant 2015 US$)'], name= 'Imports (Constant 2015)', line={'color': '#9c8141', 'width': 2}))
fig9.update_layout(
title="Nominal and Constant Exports and Imports",
xaxis_title="Year",
yaxis_title="Value in USD $")
fig9.show()
fig10 = go.Figure()
fig10.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Exports of goods and services (annual % growth)'], name= 'Exports', line={'color': '#1c421b', 'width': 2}))
fig10.add_trace(go.Scatter(x=dfEconomy.Date, y=dfEconomy['Imports of goods and services (annual % growth)'], name= 'Imports', line={'color': '#9c8141', 'width': 2}))
fig10.update_layout(
title="Exports and Imports Growth Rate",
xaxis_title="Year",
yaxis_title="Percentage %")
fig10.show()